Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different...
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The deep network model, with the majority built on neural networks, has been proved to be a powerful framework to represent complex data for high performance machine learning. In recent years, more and more studies tu...
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With the rapid development of data mining technology, multi-view learning (MVL) has become a new research field, which has attracted wide attention of scholars at home and abroad. Multi-view learning is to combine mul...
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With the rapid development of data mining technology, multi-view learning (MVL) has become a new research field, which has attracted wide attention of scholars at home and abroad. Multi-view learning is to combine multiple view data of the same entity for data classification, thereby improving learning performance. Previous multi-view research methods mainly concentrate on the relationship between different data views for classification problems. However, when the data is in high dimensions, it is necessary to perform feature selection in the multi-view data classification process. In this paper, we proposed a Multi-view Support Vector Machine Classification with Feature Selection (MSVMCFS) algorithm, which can not only classify multi-view data, but also select features for each view data in the process of classification. In the model, feature selection is performed by the l 1 norm sparsity regularization, and consistency and complementarity between the two views are maintained. To achieve the optimization goal, we adopt linear programming to solve the model. The experimental results on 30 binary datasets demonstrate the validity of the model.
Lipid nanoparticle-based drug delivery systems have a profound clinical impact on nucleic acid-based therapy and vaccination. Recombinant human insulin, a negatively-charged biomolecule like mRNA, may also be delivere...
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Lipid nanoparticle-based drug delivery systems have a profound clinical impact on nucleic acid-based therapy and vaccination. Recombinant human insulin, a negatively-charged biomolecule like mRNA, may also be delivered by rationally-designed positively-charged lipid nanoparticles with glucose-sensing elements and be released in a glucose-responsive manner. Herein, we have designed phenylboronic acid-based quaternary amine-type cationic lipids that can self-assemble into spherical lipid nanoparticles in an aqueous solution. Upon mixing insulin and the lipid nanoparticles, a heterostructured insulin complex is formed immediately arising from the electrostatic attraction. In a hyperglycemia-relevant glucose solution, lipid nanoparticles become less positively charged over time, leading to reduced attraction and subsequent insulin release. Compared with native insulin, this lipid nanoparticle-based glucose-responsive insulin shows prolonged blood glucose regulation ability and blood glucose-triggered insulin release in a type 1 diabetic mouse model.
In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective L...
In this paper, we extend the popular dictionary pair learning (DPL) into the scenario of twin-projective latent flexible DPL under a structured twin-incoherence. Technically, a novel framework called Twin-Projective Latent Flexible DPL (TP-DPL) is proposed, which minimizes the twin-incoherence constrained flexibly-relaxed reconstruction error to avoid the possible over-fitting issue and produce accurate reconstruction. In this setting, TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner. Therefore, TP-DPL can unify the procedures of salient feature representation and classification. The twin-incoherence constraint on coefficients and features can explicitly ensure high intra-class compactness and inter-class separation over them. TP-DPL also integrates the adaptive weighting to preserve local neighborhood of both coefficients and salient features within each class explicitly. For efficiency, TP-DPL selects the Frobenius-norm and abandons the costly l0/l1-norm for group sparse representation. Another byproduct is that TP-DPL can directly apply the class-specific twin-projective reconstruction residual to compute the label of data. Extensive results on public databases show that TP-DPL can deliver the state-of-the-art performance.
Entity search and exploration can enrich search user interfaces by presenting relevant information instantly and offering relevant exploration pointers to users. Previous research has demonstrated that large knowledge...
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ISBN:
(纸本)9781450348935
Entity search and exploration can enrich search user interfaces by presenting relevant information instantly and offering relevant exploration pointers to users. Previous research has demonstrated that large knowledge Graphs allow exploitation and recommendation of explicit links between the entities and other information to improve information access and ranking. However, less attention has been devoted to user interfaces for effectively presenting results, recommending related entities and explaining relations between entities. We introduce a system called SEED which is designed to support entity search and exploration in large knowledge Graphs. We demonstrate SEED using a dataset of hundreds of thousands of movie related entities from the DBpedia knowledge Graph. The system utilizes a graph embedding model for ranking entities and their relations, recommending related entities, and explaining their interrelations. Copyright is held by the author/owner(s).
The CUPID-Mo experiment to search for 0 $$\nu \beta \beta $$ decay in $$^{100}$$ Mo has been recently completed after about 1.5 years of operation at Laboratoire Souterrain de Modane (France). It served as a ...
The CUPID-Mo experiment to search for 0 $$\nu \beta \beta $$ decay in $$^{100}$$ Mo has been recently completed after about 1.5 years of operation at Laboratoire Souterrain de Modane (France). It served as a demonstrator for CUPID, a next generation 0 $$\nu \beta \beta $$ decay experiment. CUPID-Mo was comprised of 20 enriched $$\hbox {Li}_{{2}}$$ $$^{100}$$ $$\hbox {MoO}_4$$ scintillating calorimeters, each with a mass of $$\sim 0.2$$ kg, operated at $$\sim 20$$ mK. We present here the final analysis with the full exposure of CUPID-Mo ( $$^{100}$$ Mo exposure of 1.47 $$\hbox {kg} \times \hbox {year}$$ ) used to search for lepton number violation via 0 $$\nu \beta \beta $$ decay. We report on various analysis improvements since the previous result on a subset of data, reprocessing all data with these new techniques. We observe zero events in the region of interest and set a new limit on the $$^{100}$$ Mo 0 $$\nu \beta \beta $$ decay half-life of $$T_{1/2}^{0\nu }$$ $$> {1.8}\times 10^{24}$$ year (stat. + syst.) at 90% CI. Under the light Majorana neutrino exchange mechanism this corresponds to an effective Majorana neutrino mass of $$\left$$ $$<~{(0.28{-}0.49)} $$ eV, dependent upon the nuclear matrix element utilized.
The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the tra...
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In this paper, we propose a robust representation learning model called Adaptive Structure-constrained Low-Rank Coding (AS-LRC) for the latent representation of data. To recover the underlying subspaces more accuratel...
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The Lovász Local Lemma (LLL) is a very powerful tool in combinatorics and probability theory to show the possibility of avoiding all bad events under some weakly dependent conditions. In a seminal paper, Ambainis...
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